Data Visualization of NYC Airbnb
Jinqian Pan
11/23/2019
Loading Packages & Data
library(tidyverse)
library(dplyr)
library(gridExtra)
library(kableExtra)
library(ggthemr)
library(leaflet)
library(leaflet.extras)
library(wordcloud)
library(tm)
library(NLP)
library(corrplot)
ggthemr('dust')
airbnb <- read.csv("AB_NYC_2019.csv", stringsAsFactors = F, na.strings = c(""))Data exploration
Structure and features
head(airbnb) %>% kable() %>% kable_styling() %>% scroll_box(width="910px")| id | name | host_id | host_name | neighbourhood_group | neighbourhood | latitude | longitude | room_type | price | minimum_nights | number_of_reviews | last_review | reviews_per_month | calculated_host_listings_count | availability_365 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2539 | Clean & quiet apt home by the park | 2787 | John | Brooklyn | Kensington | 40.64749 | -73.97237 | Private room | 149 | 1 | 9 | 2018-10-19 | 0.21 | 6 | 365 |
| 2595 | Skylit Midtown Castle | 2845 | Jennifer | Manhattan | Midtown | 40.75362 | -73.98377 | Entire home/apt | 225 | 1 | 45 | 2019-05-21 | 0.38 | 2 | 355 |
| 3647 | THE VILLAGE OF HARLEM….NEW YORK ! | 4632 | Elisabeth | Manhattan | Harlem | 40.80902 | -73.94190 | Private room | 150 | 3 | 0 | NA | NA | 1 | 365 |
| 3831 | Cozy Entire Floor of Brownstone | 4869 | LisaRoxanne | Brooklyn | Clinton Hill | 40.68514 | -73.95976 | Entire home/apt | 89 | 1 | 270 | 2019-07-05 | 4.64 | 1 | 194 |
| 5022 | Entire Apt: Spacious Studio/Loft by central park | 7192 | Laura | Manhattan | East Harlem | 40.79851 | -73.94399 | Entire home/apt | 80 | 10 | 9 | 2018-11-19 | 0.10 | 1 | 0 |
| 5099 | Large Cozy 1 BR Apartment In Midtown East | 7322 | Chris | Manhattan | Murray Hill | 40.74767 | -73.97500 | Entire home/apt | 200 | 3 | 74 | 2019-06-22 | 0.59 | 1 | 129 |
str(airbnb)## 'data.frame': 48895 obs. of 16 variables:
## $ id : int 2539 2595 3647 3831 5022 5099 5121 5178 5203 5238 ...
## $ name : chr "Clean & quiet apt home by the park" "Skylit Midtown Castle" "THE VILLAGE OF HARLEM....NEW YORK !" "Cozy Entire Floor of Brownstone" ...
## $ host_id : int 2787 2845 4632 4869 7192 7322 7356 8967 7490 7549 ...
## $ host_name : chr "John" "Jennifer" "Elisabeth" "LisaRoxanne" ...
## $ neighbourhood_group : chr "Brooklyn" "Manhattan" "Manhattan" "Brooklyn" ...
## $ neighbourhood : chr "Kensington" "Midtown" "Harlem" "Clinton Hill" ...
## $ latitude : num 40.6 40.8 40.8 40.7 40.8 ...
## $ longitude : num -74 -74 -73.9 -74 -73.9 ...
## $ room_type : chr "Private room" "Entire home/apt" "Private room" "Entire home/apt" ...
## $ price : int 149 225 150 89 80 200 60 79 79 150 ...
## $ minimum_nights : int 1 1 3 1 10 3 45 2 2 1 ...
## $ number_of_reviews : int 9 45 0 270 9 74 49 430 118 160 ...
## $ last_review : chr "2018-10-19" "2019-05-21" NA "2019-07-05" ...
## $ reviews_per_month : num 0.21 0.38 NA 4.64 0.1 0.59 0.4 3.47 0.99 1.33 ...
## $ calculated_host_listings_count: int 6 2 1 1 1 1 1 1 1 4 ...
## $ availability_365 : int 365 355 365 194 0 129 0 220 0 188 ...summary(airbnb)## id name host_id host_name
## Min. : 2539 Length:48895 Min. : 2438 Length:48895
## 1st Qu.: 9471945 Class :character 1st Qu.: 7822033 Class :character
## Median :19677284 Mode :character Median : 30793816 Mode :character
## Mean :19017143 Mean : 67620011
## 3rd Qu.:29152178 3rd Qu.:107434423
## Max. :36487245 Max. :274321313
##
## neighbourhood_group neighbourhood latitude longitude
## Length:48895 Length:48895 Min. :40.50 Min. :-74.24
## Class :character Class :character 1st Qu.:40.69 1st Qu.:-73.98
## Mode :character Mode :character Median :40.72 Median :-73.96
## Mean :40.73 Mean :-73.95
## 3rd Qu.:40.76 3rd Qu.:-73.94
## Max. :40.91 Max. :-73.71
##
## room_type price minimum_nights number_of_reviews
## Length:48895 Min. : 0.0 Min. : 1.00 Min. : 0.00
## Class :character 1st Qu.: 69.0 1st Qu.: 1.00 1st Qu.: 1.00
## Mode :character Median : 106.0 Median : 3.00 Median : 5.00
## Mean : 152.7 Mean : 7.03 Mean : 23.27
## 3rd Qu.: 175.0 3rd Qu.: 5.00 3rd Qu.: 24.00
## Max. :10000.0 Max. :1250.00 Max. :629.00
##
## last_review reviews_per_month calculated_host_listings_count
## Length:48895 Min. : 0.010 Min. : 1.000
## Class :character 1st Qu.: 0.190 1st Qu.: 1.000
## Mode :character Median : 0.720 Median : 1.000
## Mean : 1.373 Mean : 7.144
## 3rd Qu.: 2.020 3rd Qu.: 2.000
## Max. :58.500 Max. :327.000
## NA's :10052
## availability_365
## Min. : 0.0
## 1st Qu.: 0.0
## Median : 45.0
## Mean :112.8
## 3rd Qu.:227.0
## Max. :365.0
## airbnb[,"id"] <- NULL
airbnb[,"host_id"] <- NULL
airbnb[,"host_name"] <- NULLMissing Data
missing_airbnb <- as.data.frame(table(which(is.na(airbnb),arr.ind = T)[,2]))
missing_airbnb$perc <- missing_airbnb$Freq / dim(airbnb)[1]
missing_airbnb$Var1 <- as.character(missing_airbnb$Var1)
missing_airbnb$Var1[1] <- colnames(airbnb)[as.numeric(missing_airbnb$Var1[1])]
missing_airbnb$Var1[2] <- colnames(airbnb)[as.numeric(missing_airbnb$Var1[2])]
missing_airbnb$Var1[3] <- colnames(airbnb)[as.numeric(missing_airbnb$Var1[3])]
ggplot(missing_airbnb, aes(x=missing_airbnb$Var1, y=missing_airbnb$perc)) +
geom_bar(stat = "identity", aes(color = I('white')), size = 0.3) +
coord_flip() +
ylim(0,1) +
ylab("Percentage missing") +
xlab("Column name") +
ggtitle("Missing Data", subtitle = "name has less than 0.001 percentage missing")
airbnb_name <- airbnb[,"name"]
airbnb[, "name"] <- NULL
airbnb[, "last_review"] <- NULL
airbnb[, "reviews_per_month"] <- NULLThe both column “reviews_per_month” and “last_review” are missing about 20.56%, which means these two variable have some relation. Because of 20.56% lossing, we could not use these two column. Although “name” has las than 0.001 percentage missing, we cannot fill them, so we delete those rows.
Data Visualization
Location & Price
Location and Price are the most important factors for people choosing homestay.
Location
In the data, neighbourhood_group means location and neighbourhood means area
# neighbourhood_group
airbnb$neighbourhood_group <- as.factor(airbnb$neighbourhood_group)
summary(airbnb$neighbourhood_group)## Bronx Brooklyn Manhattan Queens Staten Island
## 1091 20104 21661 5666 373NGlocation <- as.data.frame(table(airbnb$neighbourhood_group))
colnames(NGlocation) <- c('Location', 'Freq')
ggplot(NGlocation, aes(x=NGlocation$Location, y=NGlocation$Freq, fill=NGlocation$Location)) +
geom_bar(stat = "identity", aes(color = I('white')), size = 0.3) +
theme(legend.position = "none") +
xlab("Location") +
ylab("Number of Houses")
As we can see, Brookly and Manhattan are two most popular place that people rent their houses.
Area
airbnb$neighbourhood <- as.factor(airbnb$neighbourhood)
head(summary(airbnb$neighbourhood), 20)## Williamsburg Bedford-Stuyvesant Harlem Bushwick
## 3920 3714 2658 2465
## Upper West Side Hell's Kitchen East Village Upper East Side
## 1971 1958 1853 1798
## Crown Heights Midtown East Harlem Greenpoint
## 1564 1545 1117 1115
## Chelsea Lower East Side Astoria Washington Heights
## 1113 911 900 899
## West Village Financial District Flatbush Clinton Hill
## 768 744 621 572# It is to much area; but as we can see, arounding 10 Area that people are most liking to rent their house.
NArea <- as.data.frame(table(airbnb$neighbourhood))
colnames(NArea) <- c('Area', 'Freq')
NArea <- filter(NArea, Freq > 1000)
NArea <- arrange(NArea, NArea$Freq)
ggplot(NArea, aes(x=NArea$Area, y=NArea$Freq)) +
geom_bar(stat = "identity") +
theme(legend.position = "none") +
xlab("Area") +
ylab("Number of Houses") +
coord_flip()
Latitude & Longitude
nycLatLong <- data.frame(lat = airbnb$latitude, lng = airbnb$longitude)
nycLatLong %>%
leaflet() %>%
addTiles() %>%
addProviderTiles('HikeBike.HikeBike') %>%
addProviderTiles(providers$Stamen.Toner, group = "Toner") %>%
addProviderTiles(providers$CartoDB.DarkMatter, group = "DarkMatter") %>%
addProviderTiles(providers$Esri.WorldImagery, group = "ESRI-Sat") %>%
addWebGLHeatmap(lng = airbnb$longitude, lat = airbnb$latitude, size = 500) %>%
addLayersControl(
baseGroups = c("OSM (default)",
"Toner",
"DarkMatter",
"ESRI-Sat"),
options = layersControlOptions(collapsed = FALSE)
)Price
AirPrice <- as.data.frame(table(airbnb$price))
colnames(AirPrice) <- c("Price", "Freq")
ggplot(airbnb, aes(x=airbnb$price)) +
geom_density() +
ggtitle("Distribution of price",
subtitle = "The distribution is very skewed") +
xlab("Price")
The distribution is very skewed, and the graph means the most Price is under $2500, so that we change the x-axis.
AirPrice <- as.data.frame(table(airbnb$price))
colnames(AirPrice) <- c("Price", "Freq")
ggplot(airbnb, aes(x=airbnb$price)) +
geom_density() +
ggtitle("Distribution of price",
subtitle = "Mean price = $142.31") +
xlab("Price") +
scale_x_log10() +
geom_vline(xintercept = round(mean(airbnb$price), 2), size = 1)
Is any relation between price and location?
airbnb_nh <- airbnb %>%
group_by(neighbourhood_group) %>%
summarise(price = round(mean(price), 2))
ggplot(airbnb, aes(price)) +
geom_density() +
ggtitle("Transformed distribution of price\n by neighbourhood groups",
subtitle = expression("With" ~'log'[10] ~ "transformation of x-axis")) +
geom_vline(data = airbnb_nh, aes(xintercept = price), size = 1) +
geom_text(data = airbnb_nh,y = 1.5, aes(x = price + 1400, label = paste("Mean = ",price)), color = "darkgreen", size = 4) +
facet_wrap(~neighbourhood_group) +
scale_x_log10() 
Five locations have similar distribution, but Manhattan have the highest price in five locations.
Many factors can affect “Number of Review”
Name
wordcloud(airbnb_name)
In the name of the houses, hosts like use “private”, “manhattan”, “brooklyn”, “charming”, etc.
Rome Type
airbnb$room_type <- factor(airbnb$room_type)
RoomType <- as.data.frame(table(airbnb$room_type))
colnames(RoomType) <- c("RoomType","Freq")
str(RoomType)## 'data.frame': 3 obs. of 2 variables:
## $ RoomType: Factor w/ 3 levels "Entire home/apt",..: 1 2 3
## $ Freq : int 25409 22326 1160ggplot(RoomType, aes(x=2, y=Freq, fill=RoomType)) +
geom_bar(stat = "identity", color = "white", width=0.8) +
scale_fill_brewer(palette=4) +
coord_polar(theta="y") +
xlim(0.5, 2.5) +
theme_void()
Most host will rent the “Entire home/apt” and “Private room”.
ggplot(airbnb, aes(x=airbnb$room_type, y=airbnb$number_of_reviews, fill=airbnb$room_type)) +
geom_boxplot() +
geom_hline(yintercept = mean(airbnb$number_of_reviews), color = "purple", linetype = 2) +
scale_y_log10() +
theme(legend.position = "none") +
xlab("Room Type") +
ylab("Number of Reviews")
Each kinds of Room Type will have similar Number of Reviews
Multiple Regression
airMutiReg <- airbnb[,c("number_of_reviews", "neighbourhood_group", "room_type", "price", "minimum_nights", "availability_365")]
airMutiReg$neighbourhood_group <- factor(airMutiReg$neighbourhood_group)
str(airMutiReg)## 'data.frame': 48895 obs. of 6 variables:
## $ number_of_reviews : int 9 45 0 270 9 74 49 430 118 160 ...
## $ neighbourhood_group: Factor w/ 5 levels "Bronx","Brooklyn",..: 2 3 3 2 3 3 2 3 3 3 ...
## $ room_type : Factor w/ 3 levels "Entire home/apt",..: 2 1 2 1 1 1 2 2 2 1 ...
## $ price : int 149 225 150 89 80 200 60 79 79 150 ...
## $ minimum_nights : int 1 1 3 1 10 3 45 2 2 1 ...
## $ availability_365 : int 365 355 365 194 0 129 0 220 0 188 ...airMROneHot <- cbind(select_if(airMutiReg,is.numeric),as.data.frame(model.matrix(~room_type-1,airMutiReg)))
airMROneHot <- cbind(select_if(airMROneHot,is.numeric),as.data.frame(model.matrix(~neighbourhood_group-1,airMutiReg)))
cor(airMROneHot)## number_of_reviews price minimum_nights
## number_of_reviews 1.000000000 -0.04795423 -0.080116068
## price -0.047954227 1.00000000 0.042799334
## minimum_nights -0.080116068 0.04279933 1.000000000
## availability_365 0.172027581 0.08182883 0.144303063
## room_typeEntire home/apt -0.010087231 0.25585665 0.074899803
## room_typePrivate room 0.017253226 -0.24024642 -0.073836539
## room_typeShared room -0.023354904 -0.05361282 -0.004217946
## neighbourhood_groupBronx 0.009257903 -0.04102998 -0.018185701
## neighbourhood_groupBrooklyn 0.017413636 -0.09860261 -0.039658304
## neighbourhood_groupManhattan -0.045820056 0.16397551 0.067362031
## neighbourhood_groupQueens 0.035966538 -0.08020500 -0.032629026
## neighbourhood_groupStaten Island 0.015088160 -0.01383994 -0.009399622
## availability_365 room_typeEntire home/apt
## number_of_reviews 0.172027581 -0.010087231
## price 0.081828827 0.255856647
## minimum_nights 0.144303063 0.074899803
## availability_365 1.000000000 -0.006804234
## room_typeEntire home/apt -0.006804234 1.000000000
## room_typePrivate room -0.010985839 -0.953470178
## room_typeShared room 0.058293890 -0.162143592
## neighbourhood_groupBronx 0.060806165 -0.052092280
## neighbourhood_groupBrooklyn -0.079670621 -0.073905066
## neighbourhood_groupManhattan -0.005433606 0.160081940
## neighbourhood_groupQueens 0.087112501 -0.108505293
## neighbourhood_groupStaten Island 0.057884814 -0.008390983
## room_typePrivate room room_typeShared room
## number_of_reviews 0.017253226 -0.0233549043
## price -0.240246424 -0.0536128152
## minimum_nights -0.073836539 -0.0042179463
## availability_365 -0.010985839 0.0582938901
## room_typeEntire home/apt -0.953470178 -0.1621435919
## room_typePrivate room 1.000000000 -0.1428987359
## room_typeShared room -0.142898736 1.0000000000
## neighbourhood_groupBronx 0.042765002 0.0310413460
## neighbourhood_groupBrooklyn 0.079464133 -0.0174667259
## neighbourhood_groupManhattan -0.157762374 -0.0091690009
## neighbourhood_groupQueens 0.100676348 0.0266930678
## neighbourhood_groupStaten Island 0.008345062 0.0002329651
## neighbourhood_groupBronx
## number_of_reviews 0.009257903
## price -0.041029979
## minimum_nights -0.018185701
## availability_365 0.060806165
## room_typeEntire home/apt -0.052092280
## room_typePrivate room 0.042765002
## room_typeShared room 0.031041346
## neighbourhood_groupBronx 1.000000000
## neighbourhood_groupBrooklyn -0.126238876
## neighbourhood_groupManhattan -0.134729839
## neighbourhood_groupQueens -0.054692899
## neighbourhood_groupStaten Island -0.013245408
## neighbourhood_groupBrooklyn
## number_of_reviews 0.01741364
## price -0.09860261
## minimum_nights -0.03965830
## availability_365 -0.07967062
## room_typeEntire home/apt -0.07390507
## room_typePrivate room 0.07946413
## room_typeShared room -0.01746673
## neighbourhood_groupBronx -0.12623888
## neighbourhood_groupBrooklyn 1.00000000
## neighbourhood_groupManhattan -0.74524041
## neighbourhood_groupQueens -0.30252659
## neighbourhood_groupStaten Island -0.07326523
## neighbourhood_groupManhattan
## number_of_reviews -0.045820056
## price 0.163975505
## minimum_nights 0.067362031
## availability_365 -0.005433606
## room_typeEntire home/apt 0.160081940
## room_typePrivate room -0.157762374
## room_typeShared room -0.009169001
## neighbourhood_groupBronx -0.134729839
## neighbourhood_groupBrooklyn -0.745240413
## neighbourhood_groupManhattan 1.000000000
## neighbourhood_groupQueens -0.322874856
## neighbourhood_groupStaten Island -0.078193131
## neighbourhood_groupQueens
## number_of_reviews 0.03596654
## price -0.08020500
## minimum_nights -0.03262903
## availability_365 0.08711250
## room_typeEntire home/apt -0.10850529
## room_typePrivate room 0.10067635
## room_typeShared room 0.02669307
## neighbourhood_groupBronx -0.05469290
## neighbourhood_groupBrooklyn -0.30252659
## neighbourhood_groupManhattan -0.32287486
## neighbourhood_groupQueens 1.00000000
## neighbourhood_groupStaten Island -0.03174211
## neighbourhood_groupStaten Island
## number_of_reviews 0.0150881597
## price -0.0138399434
## minimum_nights -0.0093996218
## availability_365 0.0578848136
## room_typeEntire home/apt -0.0083909829
## room_typePrivate room 0.0083450616
## room_typeShared room 0.0002329651
## neighbourhood_groupBronx -0.0132454075
## neighbourhood_groupBrooklyn -0.0732652325
## neighbourhood_groupManhattan -0.0781931311
## neighbourhood_groupQueens -0.0317421076
## neighbourhood_groupStaten Island 1.0000000000corrplot(corr=cor(airMROneHot),order = "AOE")
Which means those variable have weak correlation.